This AI Cybersecurity Course at kodestree moves you from core AI risk concepts to hands-on defense engineering. You’ll red-team machine learning models, secure LLM applications against prompt injection and data leakage, map controls to NIST AI RMF and ISO/IEC 42001, and build monitoring for agentic AI systems. With mentor-led sessions, real datasets, and scenario-based labs, you’ll leave able to assess, secure, and govern AI systems in production, not just recite frameworks.
Prerequisites
This course is built to work for both newcomers and experienced security professionals. If you’re new to the field, our AI Cybersecurity for Beginners primer in Module 1 covers core AI/ML concepts and foundational security terminology before you move into certification-level content. To get the most from the rest of the course, you should ideally have:
- A basic understanding of networking and security fundamentals (firewalls, encryption, access control)
- Familiarity with how machine learning models are trained and deployed (helpful, not mandatory)
- A laptop capable of running lightweight Python notebooks for hands-on labs
- A CISM, CISSP, Security+, or equivalent credential is recommended, though not required, if you plan to pursue advanced certifications such as ISACA’s AAISM afterward
No prior AI development experience is required. If you understand basic security concepts, our labs will bring you up to speed on the AI-specific risks layered on top.
Course Objectives
- Understand how AI and machine learning systems introduce new categories of security risk
- Identify and defend against adversarial attacks, data poisoning, and model extraction
- Secure large language model (LLM) applications against prompt injection and data leakage
- Apply governance frameworks including NIST AI RMF, ISO/IEC 42001, and the OWASP Top 10 for LLM Applications
- Assess and manage risk across the AI supply chain, including third-party models and vendors
- Monitor and secure agentic AI systems operating with autonomous decision-making authority
- Prepare systematically for recognized AI security certifications, including CAISS and ISACA’s AAISM
What You Will Learn
- AI threat landscape fundamentals: how attackers exploit non-deterministic, data-driven systems
- Adversarial machine learning: evasion attacks, data poisoning, and model extraction techniques
- LLM and generative AI security: prompt injection, jailbreaking, and insecure output handling
- The OWASP Top 10 for LLM Applications and how to map mitigations to each risk
- AI governance frameworks: NIST AI RMF, ISO/IEC 42001, and EU AI Act risk tiers
- Securing the AI supply chain: model provenance, third-party APIs, and vendor risk
- Agentic AI security: identity, authorization, and guardrails for autonomous AI agents
- AI-powered threat detection: using AI for anomaly detection, SOC automation, and faster response
- Deepfake and synthetic media detection for identity verification and fraud prevention
- Zero Trust architecture applied to AI workloads and model access
- Red-teaming AI systems: structured testing using frameworks such as MITRE ATLAS
- Incident response and forensics specific to AI model compromise and data breaches
Who Should Enroll in This Course?
This AI Cybersecurity Online Training program is designed for professionals who need to secure the AI systems their organizations are rapidly deploying. It’s a strong fit if you are:
- Security Analysts and SOC professionals expanding into AI-specific threat detection
- Cybersecurity Consultants advising clients on responsible and secure AI adoption
- AI/ML Engineers who need to build security into models from design through deployment
- GRC and Risk professionals aligning AI initiatives with emerging compliance frameworks
- CISOs and IT leaders setting AI security strategy and governance policy
- Security+ or CISSP holders preparing for advanced AI security credentials such as AAISM
Skills You Will Gain
- Adversarial ML testing and model hardening
- LLM and prompt-injection defense
- AI-specific threat detection and monitoring
- Red-teaming with MITRE ATLAS methodology
- AI risk assessment and framework mapping (NIST AI RMF, ISO/IEC 42001)
- AI supply chain and vendor risk management
- Incident response planning for AI systems
- Regulatory and compliance alignment (EU AI Act and emerging standards)
Tools Covered
- MITRE ATLAS
- OWASP Top 10 for LLM Applications
- NIST AI Risk Management Framework
- Python-based adversarial ML testing notebooks
- LLM security scanning tools (prompt injection and jailbreak testing)
- SOC and SIEM platforms with AI-driven detection
- Model monitoring and MLOps security tooling
Career Outcomes
AI security has moved from a niche specialty to a core expectation for security teams, and employers are actively hiring to close the gap between AI expertise and cybersecurity expertise. Typical roles you can pursue after this training include:
- AI Security Engineer
- AI/ML Security Analyst
- Cybersecurity Consultant – AI Risk and Governance
- SOC Analyst (AI Threat Detection)
- AI Governance, Risk, and Compliance (GRC) Specialist
- Red Team Engineer – AI/ML Systems
Why Choose kodestree?
Professionals choose kodestree’s AI Cybersecurity Online Course to build defensible, framework-aligned skills fast. Here’s what you get:
- Live instructor-led online sessions
- Real-world adversarial testing labs
- Certified, industry-experienced trainers
- Flexible weekday and weekend batches
- Lifetime access to session recordings
- Resume and interview preparation support
- 24/7 learner support
- Course completion certificate
